Comment by zekrioca
17 hours ago
Very interesting project! I am wondering how it compare against OpenCL, which I think adopts the same fundamental idea (write once, run everywhere)? Is it about CUbeCL's internal optimization for Rust that happens at compile time?
This appears to be single source which would make it similar to SYCL.
Given that it can target WGPU I'm really wondering why OpenCL isn't included as a backend. One of my biggest complaints about GPGPU stuff is that so many of the solutions are GPU only, and often only target the vendor compute APIs (CUDA, ROCm) which have much narrower ecosystem support (versus an older core vulkan profile for example).
It's desirable to be able to target CPU for compatibility, debugging, and also because it can be nice to have a single solution for parallelizing all your data heavy work. The latter reduces mental overhead and permits more code reuse.
There's infrastructure in the SPIR-V compiler to be able to target both OpenCL and Vulkan, but we don't currently use it because OpenCL would require a new runtime, while Vulkan can simply use the existing wgpu runtime and pass raw SPIR-V shaders.
One thing I've never investigated is how performance OpenCL actually is for CPU. Do you happen to have any resources comparing it to a more native CPU implementation?
Who would use the OpenCL backend rather than the others targets provided ?
Makes sense. And indeed, having OpenCL as a backend would be a very interesting extension.
A lot of things happen at compile time, but you can execute arbitrary code in your kernel that executes at compile time, similar to generics, but with more flexibility. It's very natural to branch on a comptime config to select an algorithm.
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